Automated Glyco-Analysis of Cancer Related Proteins

癌症相关蛋白质的自动糖分析

• 批准号: 6961743
• 负责人: Lewis K. Pannell
• 金额: $ 14.6万
• 依托单位: UNIVERSITY OF SOUTH ALABAMA
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-18 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6961743
• 关键词: biomarker; cancer information system; cell surface receptors; computer program /software; glycoprotein biosynthesis; glycoprotein structure; glycosylation; mass spectrometry; membrane proteins; neoplastic growth; posttranslational modifications; proteomics; technology /technique development

DESCRIPTION (provided by applicant): The quote that "aberrant glycosylation is the hallmark of cancer cells" is reflected in numerous reports in the literature documenting changes in glycosylation on specific membrane proteins in cancer cells relative to normal cells. These changes have been shown to be involved in the release of cancer cells into the extracellular matrix and in the formation of metastasis. Glycosylated proteins represent a huge, almost untapped source of biomarkers, considering the wealth of evidence documenting their significance in cancer. Unique glycoforms could be used for diagnostic purposes, to target drugs at cancer cells, and for the development of immunotherapy. Despite the evolution of new mass spectrometry based methods for protein analysis, few of these involve the determination of post-translational modifications, especially glycosylation. As routine methods (e.g., MS/MS based sequencing methods) yield little light on glycosylated peptides, this proteomics research facility has established a new approach to automatically identify glycan structures on pure proteins from commercial or recombinant sources. It involves the acquisition of molecular weight only spectra and the detection of the glycosylation patterns using accurately determined mass gaps between the various glycoforms. The presence of multiple glycoforms is used to enhance the analysis rather than to confuse it. The approach has been shown to be reliable and extremely fast (taking less than one second) at identifying and characterizing such sites, including in proteins with highly complex glycosylation patterns. The aim of this proposal is to prove its utility in cancer where changes in glycosylation changes are interlaced with the progression of the disease. It will concentrate on both the cell surface proteins and those secreted from cells. Data will be compared to previously published reports where available. The glycans on previously uncharacterized proteins will be established and validated against the best hand interpreted results. The long-term aim is to make glycoanalyses routine to all cancer investigators, and the software integral to the approach will be made publicly available on a www site. This will represent the first step, with the glyco-analysis of full proteomes being an ultimate objective.

描述(由申请人提供)：引用“异常糖基化是癌细胞的标志”的说法反映在大量文献报告中，这些报告记录了癌细胞中特定膜蛋白相对于正常细胞的糖基化变化。这些变化已被证明与癌细胞释放到细胞外基质和转移的形成有关。考虑到大量证据证明糖基化蛋白在癌症中的重要性，糖基化蛋白代表着一个巨大的、几乎未被开发的生物标记物来源。独特的糖形式可以用于诊断目的，靶向癌细胞的药物，以及免疫疗法的发展。尽管新的基于质谱学的蛋白质分析方法不断发展，但很少涉及翻译后修饰的测定，尤其是糖基化。由于常规方法(例如，基于MS/MS的测序方法)对糖基化多肽的研究很少，该蛋白质组学研究机构建立了一种新的方法，可以自动从商业来源或重组来源的纯蛋白质上鉴定多糖结构。它包括获取纯分子量光谱和使用准确确定的各种糖形式之间的质量间隙来检测糖基化模式。多种糖形式的存在被用来加强分析，而不是混淆分析。该方法已被证明是可靠和极快的(花费不到一秒)来识别和表征这样的位点，包括在具有高度复杂的糖基化模式的蛋白质中。这项建议的目的是证明其在癌症中的有效性，在癌症中，糖基化变化的变化与疾病的进展交织在一起。它将集中在细胞表面蛋白质和细胞分泌的蛋白质上。数据将与以前发布的报告进行比较，如果有的话。将建立以前未确定特征的蛋白质上的葡聚糖，并根据最好的手工解释结果进行验证。长期目标是使糖分析成为所有癌症研究人员的例行公事，这一方法所必需的软件将在WWW网站上公开提供。这将是第一步，对完整蛋白质组的糖基化分析是最终目标。